An R-based predictive model for skin-sensitizing potential of substances with known structures

Abstract

Evaluation of skin-sensitizing potential is important to confirm the safety of cosmetics. As animal testing is no longer permitted, several alternative methods based on the adverse outcome pathway (AOP) approach have been reported. In addition, integrated approaches to testing and assessment (IATA), which combine the results of multiple alternative methods to assess skin sensitization potential, have been developed. We have reported an artificial neural network (ANN) model for sensitization risk assessment using commercial software, QwikNet. In the present study, we constructed a new sensitization prediction model for substances with known structures using the free and open-source software R for statistical analysis, and compared the results with those of the QwikNet model. The R model was confirmed to show similar predictive performance for estimated concentration three (EC3) which is the concentration of a test substance needed to produce a stimulation index of 3 to the QwikNet model on the same training set of 134 compounds. The accuracy, overpredicted rate, and underpredicted rate of the R model were 81.3%, 10.4%, and 8.2%, respectively, versus 79.9%, 10.4%, and 9.7% for the QwikNet model. In case studies of compounds not included in the training set, the R model showed generally good predictive ability. For less-well-predicted substances, additional in silico and read-across evaluations complemented the ANN model and improved the predictive accuracy. This study demonstrates that the ANN model is portable to the R software system. Furthermore, the combination of ANN prediction with in silico predictions and read-across taking account of substructures improves the prediction of skin-sensitizing potential in a weight-of-evidence approach.